paper-with-me

홈 › Papers

Explainable Entity-based Recommendations with Knowledge Graphs

2017-07-12 · Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen

Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is unavailable, generating explanations is still a hard problem. In this paper, we illustrate how explanations can be generated in such a scenario by leveraging external knowledge in the form of knowledge graphs. Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.

📄 PDF Abstract BibTeX arXiv:1707.05254

Code (0)

등록된 구현이 없습니다.

Tasks

Explainable RecommendationKnowledge Graphs

Similar Papers 제목 키워드 기반

Graph Reasoning for Explainable Cold Start Recommendation

2024-06-11 · Jibril Frej, Marta Knezevic, Tanja Kaser

The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems (RS). A common solution involves using Knowledge Graphs (KG) to train entity embeddings or…

Entity EmbeddingsKnowledge GraphsRecommendation Systems

Fairness-Aware Explainable Recommendation over Knowledge Graphs

2020-06-03 · Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao 외

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation …

Collaborative FilteringDecision MakingExplainable RecommendationFairness+3

Explainable Link Prediction for Emerging Entities in Knowledge Graphs

2020-05-01 · Rajarshi Bhowmik, Gerard de Melo

Despite their large-scale coverage, cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity. Link prediction can alleviate this by inferring a target entity, given a source entity and a …

Knowledge GraphsLink PredictionPredictionRepresentation Learning

Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

2020-04-15 · Chien-Chun Ni, Kin Sum Liu, Nicolas Torzec

In this paper, we describe an embedding-based entity recommendation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each other, learns complementary entity representations f…

Graph EmbeddingLearning-To-Rank

Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems

2023-12-18 · Zhangchi Qiu, Ye Tao, Shirui Pan, Alan Wee-Chung Liew

Conversational recommender systems (CRS) utilize natural language interactions and dialogue history to infer user preferences and provide accurate recommendations. Due to the limited conversation context and background k…

Knowledge GraphsLanguage ModelingLanguage ModellingRecommendation Systems+2